no code implementations • 3 Feb 2024 • Tingsong Xiao, Zelin Xu, Wenchong He, Jim Su, Yupu Zhang, Raymond Opoku, Ronald Ison, Jason Petho, Jiang Bian, Patrick Tighe, Parisa Rashidi, Zhe Jiang
Event prediction aims to forecast the time and type of a future event based on a historical event sequence.
1 code implementation • 12 Dec 2023 • Zelin Xu, Tingsong Xiao, Wenchong He, Yu Wang, Zhe Jiang
The problem is challenging due to the sparse and noisy input labels, spatial uncertainty within the label inference process, and high computational costs associated with a large number of sample locations.
1 code implementation • 18 May 2023 • Yichen Zhang, Jiehong Lin, Ke Chen, Zelin Xu, YaoWei Wang, Kui Jia
Domain gap between synthetic and real data in visual regression (e. g. 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imposes a piece-wise target manifold regularization into domain-invariant representation learning.
1 code implementation • 7 May 2022 • Zelin Xu, Yichen Zhang, Ke Chen, Kui Jia
Inspired by the success of point-pair features, the goal of this paper is to recover the 6D pose of an object instance segmented from RGB-D images by locally matching pairs of oriented points between the model and camera space.
no code implementations • 18 Dec 2020 • Zelin Xu, Ke Chen, KangJun Liu, Changxing Ding, YaoWei Wang, Kui Jia
By adapting existing ModelNet40 and ScanNet datasets to the single-view, partial setting, experiment results can verify the necessity of object pose estimation and superiority of our PAPNet to existing classifiers.
no code implementations • 26 Dec 2019 • Zelin Xu, Ke Chen, Kui Jia
Solving 6D pose estimation is non-trivial to cope with intrinsic appearance and shape variation and severe inter-object occlusion, and is made more challenging in light of extrinsic large illumination changes and low quality of the acquired data under an uncontrolled environment.
Ranked #1 on 6D Pose Estimation using RGBD on LineMOD (Mean ADD-S metric)